Sr. Manager, Data Engineering

Role overview

Sr. Manager, Data Engineering at Adobe

Responsibilities

• You will operate at the intersection of platform and product: hardening the agentic infrastructure (LLM orchestration, retrieval, tool/MCP integration, deploys, reliability) while making sure the AI solutions built on it deliver real marketing outcomes.
• Drive engineering quality — test discipline, deployment safety, observability, cost and latency management — across everything the team ships.
• Ensure solutions are grounded in real business outcomes and adopted by stakeholders — not demos that stall.
• Track record of shipping production AI/ML systems at scale — reliability, deployment safety, cost/latency awareness — not just prototypes.

Employment details

• Location: Bangalore [Karnātaka] [India]

About the employer

• Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Additional role details

• You'll grow the people on your team, set a high engineering bar, and partner across a global AI organization.
• Own delivery and reliability of the agentic AI platform: LLM orchestration, retrieval/RAG pipelines, tool and MCP integration, model routing, and evaluation.
• Set technical direction with your senior engineers; make the hard architecture calls and unblock the team (design-level involvement; not expected to write production code day-to-day).
• Lead the team that builds AI agents and workflows for marketing use cases (analytics, paid media, content-to-intent, executive reporting) on top of the platform.
• Balance platform investment against solution delivery so both advance.
• People leadership & delivery.
• Manage, coach, and grow a blended team; run hiring to build out the function.
• Own the roadmap and quarterly planning; convert ambiguous priorities into committed, sequenced delivery.
• Represent the team's work to leadership and cross-functional partners; drive alignment across a globally distributed AI organization.
• Agentic AI / platform engineers — Python, LLM orchestration, RAG, MCP/tooling, cloud-native deploys.
• Applied-AI solutions builders — wiring AI to marketing and analytics use cases.
• Data engineering — the pipelines and semantic layer the AI grounds on. ~12+ years in software / AI/ML engineering, with 4+ years managing engineering teams (including hiring and growing engineers).
• Demonstrated depth in agentic AI / LLM systems — orchestration, retrieval/RAG, tool use, agent frameworks, and evaluation of AI quality.
• Strong Python and modern cloud-native / containerized delivery.
• Ability to set a high engineering bar and make sound architecture decisions while leading primarily through the team.
• Excellent communication and stakeholder management across a globally distributed organization.
• Marketing technology or analytics domain experience — AEP / AJO / CJA, adtech, or digital marketing analytics.
• Experience with Databricks, vector databases (pgvector), graph stores (Neo4j), or similar data/AI infrastructure.
• Hands-on with agent/LLM frameworks and MCP, prompt/eval tooling, and LLM cost governance.
• Experience standing up or scaling a new AI engineering function.
• A team operating with a clear roadmap, predictable delivery, and a visibly higher quality/reliability bar.
• At least one AI solution moved from concept to in-production use, adopted by marketing/analytics stakeholders.
• Platform reliability, evaluation, and cost/observability practices established as the default, not the exception.
• Leadership Behaviors.
• Builder's bias — move fast on real problems, cut scope ruthlessly, prefer working systems over polished plans.